System and method for detecting an epileptic seizure in a prone epileptic person

ABSTRACT

A system for detecting an epileptic seizure in a prone person, comprising:
         at least one motion sensor with at least one measurement axis having fastening means for securing said motion sensor to said person;   first means for determining a first probability of at least a first state transition diagram of the nocturnal activity of a prone person with respect to data representing the measurement signals of the motion sensor, said first diagram comprising predetermined probabilities of oriented transitions between two different or identical states, the probabilities of the states of said first diagram being predetermined; and   second means for determining a second probability of at least a second state transition diagram for an epileptic seizure with respect to data representing the measurement signals of the motion sensor, said second diagram comprising predetermined probabilities of oriented transitions between two different or identical states, the probabilities of the states of said second diagram being predetermined.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a national phase application under 35 U.S.C. §371 ofPCT/EP2009/060740, filed Aug. 19, 2009, the entire contents of which areexpressly incorporated herein by reference.

BACKGROUND

1. Field of the Invention

The present invention relates to a system and method for detecting anepileptic seizure in a prone epileptic person.

Epileptic seizures are caused by dysfunctions of the brain which may bemanifested in various ways. This disorder affects between 0.5% and 1% ofthe population. 70% of these patients can control their epilepticseizures by using antiepileptic medication. For the other 30% ofpatients, surgery may be envisaged to remove the epileptic region, inother words the parts of the brain that trigger these seizures, in orderto ensure that the patient has no more seizures.

Many of the symptoms of an epileptic seizure are motor symptoms. Thesesymptoms can be recorded and analyzed using various devices such asvideo or motion sensors, in order to determine the nature of a patient'sseizure or detect a seizure for reasons of safety.

2. Description of the Related Art

There are known video processing methods for quantifying the motoractivity of a patient during a seizure. For this purpose, markers areplaced on the patient. The main advantage of this type of method is thatcameras are already in use in most hospital rooms. The problemsassociated with these methods are due to the fact that it is difficultto analyze movement automatically from a two-dimensional image, anduncertainties can arise if the marker disappears from the field of view.Furthermore, these types of method can only be used in a room where acamera is available.

Another approach to the motor characterization of epileptic seizures isthe use of inertial/magnetic sensors. These sensors have made itpossible to extract relevant data concerning human movements by theprocessing of multidimensional signals. Many applications have thus beendeveloped using these low-cost, non-invasive sensors. The best-known ofthese is undoubtedly the analysis of posture and walk, as described forexample in “A magnetometer-based approach for studying human movements”,by S. Bonnet and R. Heliot, IEEE Transactions on Biomedical Engineering,vol. 54, no. 7, 2007, which proposes a 3D magnetometer-based process forthe real-time evaluation of an inclination of the body to detectmovements such as a change of position from seated to standing. Thecharacterization of movements caused by neurological factors has alsobeen investigated: for example, accelerometers have been used forParkinson's disease and the detection of hand tremors, as described,respectively, in “The measuring set and signal processing method for thecharacterization of human hand tremor,” by A. Chwaleba, J. Jakubowskiand K. Kwiatos, CADSM, 2003, and “Triaxial accelerometry: a method forquantifying tremor and ataxia,” by J. D. Frost, IEEE Transactions onBiomedical Engineering, vol. 25, no. 49, 1978.In relation to epilepsy, the documents, “The potential value of 3daccelerometry for detection of motor seizures in severe epilepsy,” by T.Nijsen et al., Epilepsy and Behavior, vol. 7, 2005, and “Detection ofsubtle nocturnal motor activity from 3d accelerometry recordings inepilepsy patients,” by T. Nijsen et al., IEEE Transactions on BiomedicalEngineering, vol. 54, 2007, focus on the distinction between nocturnalmovements and seizure movements. Thus sensors are attached to a patientto detect a period in which motor activity occurs. One of theassumptions of this system is that the person does not read or visit thebathroom while the system is active.

These systems are used to detect long periods of motor activity, and cantherefore only operate in strictly controlled conditions; in theconditions of everyday life, their capacity to detect an epilepticseizure is very limited.

BRIEF SUMMARY

One object of the invention is to overcome the aforesaid problems, forexample to improve the accuracy of detection of an epileptic seizure ina prone person.

According to one aspect of the invention, a system is proposed fordetecting an epileptic seizure in a prone person, comprising:

-   -   at least one motion sensor sensibly fastened to said person with        at least one measurement axis;    -   a first determination module for determining a first probability        of at least a first state transition diagram of the nocturnal        activity of a prone person with respect to data representing the        measurement signals of the motion sensor, said first diagram        comprising predetermined probabilities of oriented transitions        between two different or identical states, the probabilities of        the states of said first diagram being predetermined;    -   a second determination module for determining a second        probability of at least a second state transition diagram for an        epileptic seizure with respect to data representing the        measurement signals of the motion sensor, said second diagram        comprising predetermined probabilities of oriented transitions        between two different or identical states, the probabilities of        the states of said second diagram being predetermined;    -   an association module for associating a state of said person as        a function of the probabilities of the measurement signals of        the motion sensor;    -   a first calculation module for calculating the relations (φ₁,        φ₁) between the first probability and the second probability;        and    -   a detection module for detecting an epileptic seizure when at        least one of said calculated relations (φ₁, φ₁) is below a        threshold (λ₁, λ₂).

A system of this type can be used with greater reliability to detect anepileptic seizure in a prone person.

In one embodiment, at least one of said state transition diagrams isadapted to use a hidden Markov model.

A hidden Markov model is defined by two random processes, namely a firstprocess, which is called “state” in the present application and which isnot observed, or which in other words is hidden, and a second process,which is the observation whose probability density at a given instantdepends on the value of the state at the same instant.

In this instance, a hidden Markov model is defined by:

-   -   an unobserved discrete process called the state, which can take        w values, for example five values (w=5) out of the following: a        rest activity (state 1), a slight agitation activity (state 2),        a tremor activity (state 3), an agitation activity (state 4) and        a strong agitation activity (state 5). This variable or state is        a first order Markov chain, and is therefore characterized by        the probabilities of transition from one state to another. In        this embodiment, a state transition diagram is defined by:        -   the set of w states, where w is 5 in the described example        -   the probabilities of transition from one state to another,            also called state transition probabilities:

{a _(i,j) =P(State=i|State=j)}_(i,jε[1, . . . , w]) ₂

-   -   a second observed process of the hidden Markov model is the        multidimensional signal of representative data, or, in other        words, the signal of characteristics extracted from the observed        signal and having a probability density depending on the state        (the hidden process) at a given instant. Let O(n) denote this        multidimensional signal at the instant n, and let        {b_(i)(O(n))=P(O(n)|State=i)}_(iε[1, . . . , w]) denote the w        probabilities associated with this signal, as a function of the        underlying hidden state.

A hidden Markov model is defined by the set pairModel={{a_(i,j)}_(i,j),{b_(i)}_(i)}. The match between the observedsignal and a given model is evaluated by the following probability:P(O(0), . . . , O(N−1)|Model)

This probability can be estimated by the conventional prior art methodsas described in “An introduction to hidden Markov models” by L. R.Rabiner and B. H. Juang, IEEE ASSP Magazine, January 1986, and in thebook “Inference in Hidden Markov Models” by Cappé, Moulines and Ryden,published by Springer, in the “Springer series in statistics”.

A number of models or state transition diagrams are defined,corresponding to different relevant movements, for example the firstnocturnal movement state transition diagram and two second statetransition diagrams, for tonic seizure movements and clonic seizuremovements.

In a tonic seizure, muscles contract and relax rapidly, creatingtremors, while in a clonic seizure there is strong agitation with suddenlarge movements.

The various probabilities J_(k)=P(O(0), . . . , O(N−1)Model_(k)) for agiven set of observations are calculated. Model_(k) represents the setof parameters describing model k. An alarm can be triggered if theprobabilities associated with seizure models are greater, by an amountabove a certain threshold, than the probabilities associated withmovements of other kinds. In other words, an epileptic seizure isdetected when at least one of the calculated relations is below athreshold, where a relation is the ratio of a first probability to asecond probability.

If the set of models associated with epileptic seizure movements isdenoted by C and the set of “normal” movements is denoted by N, anepileptic seizure is detected when the following condition is met:

∃iεC such that ∀jεN,J _(j) /J _(i)<λ_(i,j)

λ_(i,j) being an ad hoc threshold.

In one embodiment, the system additionally comprises:

-   -   a filter for selecting, for each measurement axis of the motion        sensor, high frequencies above a first threshold, and low        frequencies below a second threshold which is lower than or        equal to said first threshold;    -   second means for calculating a first value equal to a linear        combination of the respective variations along each measurement        axis, between two successive time intervals, of said low        frequencies per time interval;    -   third means for calculating a second value equal to the mean of        the energies, along each measurement axis, of said high        frequencies;    -   third means for determining the probability of said first value        defined by a normal centered Gaussian distribution; and    -   fourth means for determining the probability of said second        value defined by a Chi 2 distribution with a degree of freedom        equal to the number of measurement axes of the motion sensor        taken into consideration;        said means of association being adapted to use the probabilities        of said low and high frequencies. In other words, the means of        association are adapted to use the probabilities relative to the        first and second values, defined respectively by the third and        fourth means of determination. These means of association can be        used to assign a probability of occurrence to each state, as a        function of the first value and the second value which are        found, respectively, by the second and third means of        calculation.

The accuracy of detection is thus improved at a lower cost. Themultidimensional signal O(n) and the associated probabilities{b_(i)(O(n))=P(O(n)|State=i)}_(iε[1, . . . , w]) are defined as follows,with w=5, for example. The signal O(n) of characteristics extracted fromthe signal is of dimension 2. Its first component x(n) is the firstvalue. Its second component y(n) is the second value. Each new samplecorresponds to a value calculated over a time interval which may be 1 s,corresponding to a sampling frequency of 1 Hz, but as a general rule thesampling frequency can vary from 0.1 to 10 Hz, and preferably from 0.5Hz to 4 Hz. Experimental trials have shown that a frequency of 1 Hz issatisfactory.

For a given type of movement p, the probability of observation isdefined thus: P(O(n)|Movement_p)=P_(LF) ^((p))(x(n))·P_(HF) ^((p))(y(n))P_(LF) ^((p))(x(n)) is the probability density relative to the firstvalue; x(n) is the movement p, where p is a natural integer which is,for example, in the range from 1 to 50, and typically from 3 to 30; andP_(HF) ^((p))(y(n)) is the probability density relative to the secondvalue y(n) corresponding to the movement p.

In one embodiment, the probability density of obtaining a pair of valuesfor the low frequency component and the high frequency component isequal to the product of the probability density of obtaining the valuefor the low frequency component and the probability density of obtainingthe value for the high frequency component, and therefore saidprobability densities (P_(LF)(x), P_(HF)(x)) are defined by thefollowing expressions, for each type of movement p:

$\quad\left\{ \begin{matrix}{{P_{LF}^{(p)}\left( {x(n)} \right)} = {\frac{1}{\sqrt{2\; \pi}\sigma_{x}^{(p)}} \cdot ^{- \frac{{x{(n)}}^{2}}{2\; \sigma_{x}^{{(p)}^{2}}}}}} \\{{P_{HF}^{(p)}\left( {y(n)} \right)} = {\frac{1}{\sqrt{2^{k}}\sigma_{y}^{{(p)}^{k}}{\Gamma \left( \frac{k}{2} \right)}}{y(n)}^{\frac{k}{2} - 1}^{- \frac{y{(n)}}{2\; \sigma_{y}^{{(p)}^{2}}}}}}\end{matrix} \right.$

in which:

-   -   k represents the degree of freedom of the high frequency        component (HF) equal to the number of measurement axes of said        motion sensor (CM) taken into consideration;    -   σ_(x) ^((p)) represents the variance of x, representing a type        of movement p;    -   σ_(y) ^((p)) represents the mean of the square roots of the        energies of the high frequency components of the measurement        axes considered, representing a type of movement p;    -   n represents the sample index; and    -   Γ is the gamma function obeying the rule

${{\Gamma \left( \frac{1}{2} \right)} = \sqrt{\pi}},{{\Gamma (1)} = 1}$

and Γ(z+1)=zΓ(z) where z is real.

Thus the real probability densities of the observed signals areapproximated by probability densities globally adapted to most of themovements.

The number of pairs (σ_(x) ^((p)),σ_(y) ^((p))) and the respectivevalues of these pairs are selected so as to describe a sufficientlycomprehensive quantity of movements. The states, identified by the indexi, are then described as more or less probable movements, by thefollowing model:

$\begin{matrix}{b_{i}\left( {{O(n)} = \left\lbrack {{x(n)},{y(n)}} \right\rbrack^{T}} \right)} \\{= {\sum\limits_{p}{\alpha_{i,p}{P\left( {O(n)} \middle| {Movement\_ p} \right)}}}} \\{= {\sum\limits_{p}{\alpha_{i,p}{\frac{1}{\sqrt{2\; \pi}\sigma_{x}^{(p)}} \cdot ^{- \frac{{x{(n)}}^{2}}{2\; \sigma_{x}^{{(p)}^{2}}}}} \times \frac{1}{\sqrt{2^{k}}\sigma_{y}^{{(p)}^{k}}{\Gamma \left( \frac{k}{2} \right)}}{y(n)}^{\frac{k}{2} - 1}^{- \frac{y{(n)}}{2\; \sigma_{y}^{{(p)}^{2}}}}}}}\end{matrix}$

The coefficients α_(i,p) comply with the following constraint:

${\sum\limits_{p}\alpha_{i,p}} = 1.$

Thus the probability densities associated with the states are describedin a highly accurate way, and the observed signals are modeled in areasonably detailed way. Thus, when the person is at rest, a brief jerkdoes not have a zero probability. A jerk may also occur if the person isin a tremor state. However, this elementary movement will last longer inthis state. This model enables these subtleties to be taken intoaccount.

For example, the number of pairs (σ_(x) ^((p)),σ_(y) ^((p))) is 18,enabling 18 movements to be described.

For example, said 18 pairs are obtained by combining the followingvalues:

σ_(x)[0]=5×10⁻³, σ_(x)[1]=1.8×10⁻², σ_(x)[2]=3.5×10⁻²,σ_(x)[3]=5×0.510⁻², σ_(x)[4]=8×10⁻², σ_(x)[5]=1×10⁻¹, andσ_(y)[0]=1×10⁻², σ_(y)[1]=3×10⁻², σ_(y)[2]=8×10⁻².

If p is an index such that p=m+6n, the pairs (σ_(x) ^((p)),σ_(y) ^((p)))are defined as follows: (σ_(x) ^((p)),σ_(y) ^((p)))=(σ_(x)[m],σ_(y)[n])

In one embodiment, where 5 states are considered (w=5), saidcoefficients α_(i,p) are defined as below, varying equally from 0 to 17:

i = 2 i = 5 (slight i = 3 i = 4 (strong α_(i,p) i = 1 (rest) agitation)(tremors) (agitation) agitation) p = 0 0.2564 0 0 0 0 1 0.0513 0.0526 00 0 2 0.02564 0 0.04 0 0 3 0.2564 0.1579 0.04 0 0 4 0.0513 0.2632 0.16 00 5 0 0.0526 0.20 0 0 6 0.2564 0.1579 0.04 0 0 7 0.0513 0.2632 0.160.0926 0 8 0 0.0526 0.20 0.0926 0 9 0.0256 0 0 0.0370 0 10  0 0 0 0.18520 11  0 0 0.16 0.1852 0 12  0.0256 0 0 0.037 0.0556 13  0 0 0 0.18520.0556 14  0 0 0 0.1852 0.0556 15  0 0 0 0 0.2778 16  0 0 0 0 0.2778 17 0 0 0 0 0.2778

In one embodiment, the first state transition diagram for generalnocturnal activity is defined as follows. α_(i,j) ^(n) represents theprobability of transition from state i to state j, P(State=i|State=j),for the n-th state transition diagram.

a_(i,j) ⁽¹⁾ i = 1 i = 2 i = 3 i = 4 i = 5 j = 1 0.9 0.025 0.025 0.0250.025 j = 2 0.025 0.9 0.025 0.025 0.025 j = 3 0.025 0.025 0.9 0.0250.025 j = 4 0.025 0.025 0.025 0.9 0.025 j = 5 0.025 0.025 0.025 0.0250.9

In one embodiment, a second state transition diagram corresponding to anepileptic seizure with clonic manifestations is defined thus:

a_(i,j) ⁽²⁾ i = 1 i = 2 i = 3 i = 4 i = 5 j = 1 0 0.3 0.7 0 0 j = 2 00.9 0.1 0 0 j = 3 0 0.1 0.9 0 0 j = 4 0 0.3 0.7 0 0 j = 5 0 0.3 0.7 0 0

In one embodiment, a second state transition diagram for an epilepticseizure with tonic manifestations is defined thus:

a_(i,j) ⁽³⁾ i = 1 i = 2 i = 3 i = 4 i = 5 j = 1 0 0 0.7 0 0.3 j = 2 0 00.7 0 0.3 j = 3 0 0 0.9 0 0.1 j = 4 0 0 0.7 0 0.3 j = 5 0 0 0.3 0 0.7

In one embodiment, said second means of determination are adapted todetermine a second probability of a second state transition diagram foran epileptic seizure with clonic manifestations and of a second statetransition diagram for an epileptic seizure with tonic manifestations.Tonic manifestations include tremors, and clonic manifestations includeagitation.

In one embodiment, the states and the probabilities of the states areidentical for the first state transition diagram and for the secondstate transition diagrams.

Thus the process of implementation is simplified.

In this way, three state transition diagrams of signals are defined withthe five states (rest, slight agitation, tremor, agitation, strongagitation). A first of these diagrams describing “general” nocturnalactivity, a second describing motor manifestations of tonic epilepticseizures (with tremors) and a third describing motor manifestations ofclonic epileptic seizures (with agitation).

In one embodiment, there is only one first state transition diagram,corresponding to general nocturnal activity, defined thus:

a_(i,j) ⁽¹⁾ i = 1 i = 2 i = 3 i = 4 i = 5 j = 1 0.9 0.025 0.025 0.0250.025 j = 2 0.025 0.9 0.025 0.025 0.025 j = 3 0.025 0.025 0.9 0.0250.025 j = 4 0.025 0.025 0.025 0.9 0.025 j = 5 0.025 0.025 0.025 0.0250.9

In one embodiment, a second state transition diagram corresponding to anepileptic seizure with clonic manifestations is defined thus:

a_(i,j) ⁽²⁾ i = 1 i = 2 i = 3 i = 4 i = 5 j = 1 0 0.3 0.7 0 0 j = 2 00.9 0.1 0 0 j = 3 0 0.1 0.9 0 0 j = 4 0 0.3 0.7 0 0 j = 5 0 0.3 0.7 0 0

In one embodiment, another second state transition diagram for anepileptic seizure with tonic manifestations is defined thus:

a_(i,j) ⁽³⁾ i = 1 i = 2 i = 3 i = 4 i = 5 j = 1 0 0 0.7 0 0.3 j = 2 0 00.7 0 0.3 j = 3 0 0 0.9 0 0.1 j = 4 0 0 0.7 0 0.3 j = 5 0 0 0.3 0 0.7

Thus, the three models are defined with the three state transitiondiagrams (the first state transition diagram for general nocturnalactivity and the two second state transition diagrams for an epilepticseizure with clonic and tonic manifestations respectively), the fivedefined states, the multidimensional signal O(n) and the associatedprobabilities {b_(i)(O(n))=P(O(n)|State=i)}_(iε[1, . . . , 5]).

Thus the process of implementation is simplified.

For a given observation, corresponding to a time interval ranging fromseveral seconds to several minutes, for example 45 s, corresponding to Nmeasured signals O(n), with indices from O(0) to O(N−1), the followingthree probabilities are calculated:

J ₁ =P(O(0), . . . ,O(N−1)|Model₁)

J ₂ =P(O(0), . . . ,O(N−1)|Model₂)

J ₃ =P(O(0), . . . ,O(N−1)|Model₃)

together with the following two ratios:

$\phi_{1} = {{\frac{J_{1}}{J_{2}}\mspace{14mu} {and}\mspace{14mu} \phi_{2}} = \frac{J_{1}}{J_{3}}}$

As a general rule, J_(i) represents the probability of the observations,given the model i. J_(i) is close to 0 if the observation does not matchthe model i. Conversely, J_(i) is close to 1 if the observation doesmatch the model i.

An alarm is triggered if φ₁<λ₁ or φ₂<λ₂, where λ₁ and λ₂ are thresholdschosen on an ad hoc basis. For example, λ₁=1×10⁻² and λ₂=1×10⁻⁴.

In one embodiment, the system also comprises alerting means adapted forproviding a warning of the detection of an epileptic seizure.

These alerting means can alert persons in the vicinity or remotely, byusing an audible or visual alert for example.

In one embodiment, said motion sensor comprises an accelerometer, amagnetometer or a gyrometer.

According to another aspect of the invention, a method is also proposedfor detecting an epileptic seizure in a prone person, wherein:

-   -   a first probability of at least a first state transition diagram        of the nocturnal activity of a prone person is determined, with        respect to the measurement signals of a motion sensor with at        least one measurement axis having fastening means for securing        said motion sensor to said person, said first diagram comprising        predetermined probabilities of oriented transitions between two        different or identical states, the probabilities of the states        of said first diagram being predetermined;    -   a second probability of at least a second state transition        diagram for an epileptic seizure is determined, with respect to        the measurement signals of the motion sensor, a second diagram        comprising predetermined probabilities of oriented transitions        between two different or identical states, the probabilities of        the states of said second diagram being predetermined;    -   a state of said person is associated as a function of the        probabilities of the measurement signals of the motion sensor        (CM);    -   relations of the second probability and the first probability        are calculated; and    -   an epileptic seizure is detected when at least one of said        calculated relations is below a threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be more clearly understood by the examination of anumber of embodiments described by way of non-limiting examples andillustrated in the attached drawings, in which FIGS. 1 and 2 show twoexemplary embodiments of a system for detecting an epileptic seizure ina prone person, according to one aspect of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 shows a system for detecting an epileptic seizure in a proneperson, comprising at least one motion sensor CM with at least onemeasurement axis having fastening elements MF, comprising for example anelastic element, for securing a casing BT comprising the motion sensorCM to said person. The motion sensor CM can be an accelerometer, amagnetometer or a gyrometer, with one, two or three measurement axes.Clearly, the system can comprise a plurality of motion sensors CM.

The system comprises an association module ASS for associating a stateof said person as a function of the probabilities of the measurementsignals of the motion sensor CM, and a first determination module DET1for determining a first probability of a first state transition diagramof the nocturnal activity of a prone person with respect to datarepresenting the measurement signals of the motion sensor CM. The firstdiagram comprises predetermined probabilities of oriented transitionsbetween two different or identical states, the probabilities of thestates of said first diagram being predetermined.

The system also comprises a second determination module DET2 fordetermining a second probability of at least one second state transitiondiagram for an epileptic seizure with respect to data representing themeasurement signals of the motion sensor CM. A second diagram comprisespredetermined probabilities of oriented transitions between twodifferent or identical states, the probabilities of the states of saidsecond diagram being predetermined.

The system also comprises a first calculation module CALC1 forcalculating the relations of the first probability and the secondprobability, and a module DETEC for detecting an epileptic seizure whenat least one of said calculated relations is below a threshold.

In the remainder of the description, it is assumed, by way of example,that the second determination module DET2 uses one second statetransition diagram for an epileptic seizure with clonic manifestationsand one second state transition diagram for an epileptic seizure withtonic manifestations.

The system can include an alert or alarm module AL for providing awarning of the detection of an epileptic seizure remotely or in thevicinity, enabling people to intervene.

In a variant, as shown in FIG. 2, numerous elements may be included in aportable computer OP, for example, instead of in the casing BT as shownin FIG. 1.

The first state transition diagram of the nocturnal activity of a proneperson can have the following probabilities of transitions between twoof said five states:

a_(i,j) ⁽¹⁾ i = 1 i = 2 i = 3 i = 4 i = 5 j = 1 0.9 0.025 0.025 0.0250.025 j = 2 0.025 0.9 0.025 0.025 0.025 j = 3 0.025 0.025 0.9 0.0250.025 j = 4 0.025 0.025 0.025 0.9 0.025 j = 5 0.025 0.025 0.025 0.0250.9

A first example of a second state transition diagram for an epilepticseizure with clonic manifestations can be defined by the followingprobabilities of transition:

a_(i, j) ⁽²⁾ i = 1 i = 2 i = 3 i = 4 i = 5 j = 1 0 0.3 0.7 0 0 j = 2 00.9 0.1 0 0 j = 3 0 0.1 0.9 0 0 j = 4 0 0.3 0.7 0 0 j = 5 0 0.3 0.7 0 0

A second example of a second state transition diagram for an epilepticseizure with tonic manifestations can be defined by the followingprobabilities of transition:

a_(i,j) ⁽³⁾ i = 1 i = 2 i = 3 i = 4 i = 5 j = 1 0 0 0.7 0 0.3 j = 2 0 00.7 0 0.3 j = 3 0 0 0.9 0 0.1 j = 4 0 0 0.7 0 0.3 j = 5 0 0 0.3 0 0.7

Based on the measurement signals of the motion sensor CM, theassociation module ASS identifies the state of said person among the setof states. The first determination module DET1 determines a firstprobability of the first state transition diagram with respect to datarepresenting the measurement signals of the motion sensor CM.

Additionally, the second determination module DET2 determines a secondprobability of the second state transition diagram for an epilepticseizure with clonic manifestations with respect to data representing themeasurement signals of the motion sensor CM, and determines a secondprobability of the second state transition diagram for an epilepticseizure with tonic manifestations with respect to the measurementsignals of the motion sensor CM.

The first calculation module CALC1 estimates the relation φ₁ of thefirst probability of the first state transition diagram and the secondprobability of the second state transition diagram for an epilepticseizure with clonic manifestations, as well as the relation φ₂ of thefirst probability of the first state transition diagram and the secondprobability of the second state transition diagram for an epilepticseizure with tonic manifestations.

The detection module DETEC detects an epileptic seizure when at leastone of the two relations calculated by the first calculation moduleCALC1 is below a threshold λ₁ or λ₂ respectively.

In the remainder of the description, the examples described use the lowfrequency components LF and the high frequency components HF, and thereare two motion sensors CM, in the form of two three-axis accelerometers,a first of which is fastened to one wrist of a user, while the second isfastened to the user's other wrist or to his chest. A computer OP, asshown for example in FIG. 2, receives and records the data, detects anepileptic seizure and triggers an alert if an epileptic seizure isdetected. Additionally, the state transition diagrams which have beendescribed are adapted so that each of them uses a hidden Markov model.

At least one of said state transition diagrams can be adapted to use ahidden Markov model. The system may also comprise a filter FILT forselecting, for each measurement axis of the motion sensor CM, highfrequencies HF above a first threshold S1, and low frequencies LF belowa second threshold S2 which is lower than or equal to said firstthreshold S1. The system may also comprise a second calculation moduleCALC2 for calculating a first value x equal to a linear combination ofthe respective variations along each measurement axis, between twosuccessive time intervals, of the low frequencies LF per time intervaln, and may comprise a third calculation module CALC3 for calculating asecond value y equal to the mean of the energies, along each measurementaxis, of the high frequencies HF. Additionally, the system may comprisea third determination module DET3 for determining the probability PLF(x)of said first value x defined by a normal centered Gaussiandistribution, and a fourth determination module DET4 for determining theprobability PHF(x) of said second value y defined by a Chi 2distribution with a degree of freedom equal to the number of measurementaxes of the motion sensor CM taken into consideration. The means ofassociation ASS are adapted to use the probabilities of the low and highfrequencies LF and HF.

The probability density P(x(n),y(n)) of obtaining a pair of values(x(n), y(n)) for the low frequency component LF and the high frequencycomponent HF being equal to the product of the probability densityP_(LF)(x) of obtaining the value x(n) for the low frequency component LFand the probability density P_(HF)(x) of obtaining the value y(n) forthe high frequency component HF, the probability densities P_(LF)(x),P_(HF)(x) being defined by the following expressions, for each type ofmovement p:

$\quad\left\{ \begin{matrix}{{P_{LF}^{(p)}\left( {x(n)} \right)} = {\frac{1}{\sqrt{2\; \pi}\sigma_{x}^{(p)}} \cdot ^{- \frac{{x{(n)}}^{2}}{2\; \sigma_{x}^{{(p)}^{2}}}}}} \\{{P_{HF}^{(p)}\left( {y(n)} \right)} = {\frac{1}{\sqrt{2^{k}}\sigma_{y}^{{(p)}^{k}}{\Gamma \left( \frac{k}{2} \right)}}{y(n)}^{\frac{k}{2} - 1}^{- \frac{y{(n)}}{2\; \sigma_{y}^{{(p)}^{2}}}}}}\end{matrix} \right.$

wherein:

k represents the degree of freedom of the high frequency component (HF)equal to the number of measurement axes of said motion sensor (CM) takeninto consideration;

-   -   σ_(x) ^((p)) represents the variance of x, representing a type        of movement p;    -   σ_(y) ^((p)) represents the mean of the square roots of the        energies of the high frequency components of the measurement        axes considered, representing a type of movement p;    -   n represents the sample index; and    -   Γ is the gamma function obeying the rule

${{\Gamma \left( \frac{1}{2} \right)} = \sqrt{\pi}},{{\Gamma (1)} = 1}$

and Γ(z+1)=zΓ(z) where z is real.

Thus the present invention can significantly improve the detection of anepileptic seizure in a prone person, at a lower cost.

1. A system for detecting an epileptic seizure in a prone personcomprising: at least one motion sensor sensibly fastened to said personwith at least one measurement axis; a first determination module fordetermining a first probability of at least a first state transitiondiagram of the nocturnal activity of a prone person with respect to datarepresenting the measurement signals of the motion sensor, said firstdiagram comprising predetermined probabilities of oriented transitionsbetween two different or identical states, the probabilities of thestates of said first diagram being predetermined; a second determinationmodule for determining a second probability of at least a second statetransition diagram for an epileptic seizure with respect to datarepresenting the measurement signals of the motion sensor, said seconddiagram comprising predetermined probabilities of oriented transitionsbetween two different or identical states, the probabilities of thestates of said second diagram being predetermined; an association modulefor associating a state of said person as a function of theprobabilities of the measurement signals of the motion sensor; a firstfor calculation module for calculating the relations (φ₁, φ₁) betweenthe first probability and the second probability; and a detection modulefor detecting an epileptic seizure when at least one of said calculatedrelations (φ₁, φ₁) is below a threshold (λ₁, λ₂).
 2. The system of claim1, wherein at least one of said state transition diagrams is adapted touse a hidden Markov model.
 3. The system of claim 2, further comprising:a filter for selecting, for each measurement axis of the motion sensor,high frequencies above a first threshold, and low frequencies below asecond threshold which is lower than or equal to said first threshold; asecond calculation module for calculating a first value x equal to alinear combination of the respective variations along each measurementaxis, between two successive time intervals, of said low frequencies pertime interval n; a third calculation module for calculating a secondvalue y equal to the mean of the energies, along each measurement axis,of said high frequencies; a third determination module for determiningthe probability P_(LF)(x) of said first value x defined by a normalcentered Gaussian distribution; and a fourth determination module fordetermining the probability P_(HF)(x) of said second value y defined bya Chi 2 distribution with a degree of freedom equal to the number ofmeasurement axes of the motion sensor taken into consideration; said ofassociation module being adapted to use the probabilities of said lowand high frequencies.
 4. The system of claim 3, wherein the probabilitydensity P(x(n),y(n)) of obtaining a pair of values (x(n), y(n)) for thelow frequency component and the high frequency component being equal tothe product of the probability density P_(LF)(x) of obtaining the valuex(n) for the low frequency component and the probability densityP_(HF)(x) of obtaining the value y(n) for the high frequency component,said probability densities P_(LF)(x) and P_(HF)(x) being defined by thefollowing expressions: $\quad\left\{ \begin{matrix}{{P_{LF}^{(p)}\left( {x(n)} \right)} = {\frac{1}{\sqrt{2\; \pi}\sigma_{x}^{(p)}} \cdot ^{- \frac{{x{(n)}}^{2}}{2\; \sigma_{x}^{{(p)}^{2}}}}}} \\{{P_{HF}^{(p)}\left( {y(n)} \right)} = {\frac{1}{\sqrt{2^{k}}\sigma_{y}^{{(p)}^{k}}{\Gamma \left( \frac{k}{2} \right)}}{y(n)}^{\frac{k}{2} - 1}^{- \frac{y{(n)}}{2\; \sigma_{y}^{{(p)}^{2}}}}}}\end{matrix} \right.$ wherein: k represents the degree of freedom of thehigh frequency component equal to the number of measurement axes of saidmotion sensor taken into consideration; σ_(x) ^((p)) represents thevariance of x, representing a type of movement p; σ_(y) ^((p))represents the mean of the square roots of the energies of the highfrequency components of the measurement axes considered, representing atype of movement p; n represents the sample index; and Γ is the gammafunction obeying the rule${{\Gamma \left( \frac{1}{2} \right)} = \sqrt{\pi}},{{\Gamma (1)} = 1}$and Γ(z+1)=zΓ(z) where z is real.
 5. The system of claim 2, wherein saidhidden Markov model comprises not more than five states chosen from agroup comprising a rest activity, a slight agitation activity, a tremoractivity, an agitation activity, and a strong agitation activity.
 6. Thesystem of claim 2, wherein the probabilities of the states of said statetransition diagrams are defined by the following relation:$\begin{matrix}{b_{i}\left( {{O(n)} = \left\lbrack {{x(n)},{y(n)}} \right\rbrack^{T}} \right)} \\{= {\sum\limits_{p}{\alpha_{i,p}{\frac{1}{\sqrt{2\; \pi}\sigma_{x}^{(p)}} \cdot ^{- \frac{{x{(n)}}^{2}}{2\; \sigma_{x}^{{(p)}^{2}}}}} \times}}} \\{= {\frac{1}{\sqrt{2^{k}}\sigma_{y}^{{(p)}^{k}}{\Gamma \left( \frac{k}{2} \right)}}{y(n)}^{\frac{k}{2} - 1}^{- \frac{y{(n)}}{2\; \sigma_{y}^{{(p)}^{2}}}}}}\end{matrix}$ wherein the value of a pair (σ_(x) ^((p)), σ_(y) ^((p)))depends on the description of a movement, and the coefficients α_(i,p)comply with the following constraint:${\sum\limits_{p}\alpha_{i,p}} = 1.$
 7. The system of claim 1, whereinsaid second determination module is adapted to determine a secondprobability of a second state transition diagram for an epilepticseizure with clonic manifestations and of a second state transitiondiagram for an epileptic seizure with tonic manifestations.
 8. Thesystem of claim 1, wherein the states and the probabilities of thestates are identical for the first state transition diagram and for thesecond state transition diagrams.
 9. The system as claimed of claim 6,wherein there are 18 of said pairs (σ_(x) ^((i)), o_(y) ^((i))), whichare obtained by the combination of the following values:σ_(x)[0]=5×10⁻³, σ_(x)[1]=1.8×10⁻², σ_(x)[2]=3.5×10⁻²,σ_(x)[3]=5×0.510⁻², σ_(x)[4]=8×10⁻², σ_(x)[5]=1×10⁻¹, andσ_(y)[0]=1×10⁻², σ_(y)[1]=3×10⁻², σ_(y)[2]=8×10⁻².
 10. The system ofclaim 9, wherein said coefficients α_(i,p) are defined as follows, for apair (σ_(x) ^((p))[m], σ_(y) ^((p))[n]), where p is an index such thatp=m+6n, varying from 0 to 17 for the 18 pairs (σ_(x) ^((p)), σ_(y)^((p))): i = 5 i = 2 (slight i = 3 i = 4 (strong α_(i,p) i = 1 (rest)agitation) (tremors) (agitation) agitation) p = 0 0.2564 0 0 0 0 10.0513 0.0526 0 0 0 2 0.02564 0 0.04 0 0 3 0.2564 0.1579 0.04 0 0 40.0513 0.2632 0.16 0 0 5 0 0.0526 0.20 0 0 6 0.2564 0.1579 0.04 0 0 70.0513 0.2632 0.16 0.0926 0 8 0 0.0526 0.20 0.0926 0 9 0.0256 0 0 0.03700 10  0 0 0 0.1852 0 11  0 0 0.16 0.1852 0 12  0.0256 0 0 0.037 0.055613  0 0 0 0.1852 0.0556 14  0 0 0 0.1852 0.0556 15  0 0 0 0 0.2778 16  00 0 0 0.2778 17  0 0 0 0 0.2778


11. The system of claim 10, wherein a first state transition diagram ofgeneral nocturnal activity is defined by the following matrix: a_(i,j)⁽¹⁾ i = 1 i = 2 i = 3 i = 4 i = 5 j = 1 0.9 0.025 0.025 0.025 0.025 j =2 0.025 0.9 0.025 0.025 0.025 j = 3 0.025 0.025 0.9 0.025 0.025 j = 40.025 0.025 0.025 0.9 0.025 j = 5 0.025 0.025 0.025 0.025 0.9


12. The system of claim 10, wherein a second state transition diagramfor an epileptic seizure with clonic manifestations is defined by thefollowing matrix: a_(i,j) ⁽²⁾ i = 1 i = 2 i = 3 i = 4 i = 5 j = 1 0 0.30.7 0 0 j = 2 0 0.9 0.1 0 0 j = 3 0 0.1 0.9 0 0 j = 4 0 0.3 0.7 0 0 j =5 0 0.3 0.7 0 0


13. The system of claim 10, wherein a second state transition diagramfor an epileptic seizure with tonic manifestations is defined by thefollowing matrix: a_(i,j) ⁽³⁾ i = 1 i = 2 i = 3 i = 4 i = 5 j = 1 0 00.7 0 0.3 j = 2 0 0 0.7 0 0.3 j = 3 0 0 0.9 0 0.1 j = 4 0 0 0.7 0 0.3 j= 5 0 0 0.3 0 0.7


14. The system of claim 1, further comprising an alerting module adaptedfor providing a warning of the detection of an epileptic seizure. 15.The system of claim 1, wherein said motion sensor comprises anaccelerometer, a magnetometer or a gyrometer.
 16. A method for detectingan epileptic seizure in a prone person, wherein: a first probability ofat least a first state transition diagram of the nocturnal activity of aprone person is determined, with respect to the measurement signals of amotion sensor sensibly fastened to said person with at least onemeasurement axis, said first diagram comprising predeterminedprobabilities of oriented transitions between two different or identicalstates, the probabilities of the states of said first diagram beingpredetermined; a second probability of at least a second statetransition diagram for an epileptic seizure is determined, with respectto the measurement signals of the motion sensor, where said seconddiagram comprises predetermined probabilities of oriented transitionsbetween two different or identical states, the probabilities of thestates of said second diagram being predetermined; a state of saidperson is associated as a function of the probabilities of themeasurement signals of the motion sensor; relations (φ₁, φ₁) of thefirst probability and the second probability are calculated; and anepileptic seizure is detected when at least one of said calculatedrelations (φ₁, φ₁) is below a threshold (λ₁, λ₂).